Affective computing with eye-tracking data in the study of the visual perception of architectural spaces

¹ Lublin University of Technology, Institute of Computer Science, Nadbystrzycka 36B, 20-618 Lublin, Poland
² Medical University of Lublin, Clinical Genetics Department, Radziwiłłowska 11, 20-080 Lublin, Poland
³ Lublin University of Technology, Department of Architecture and Urban Planning, Nadbystrzycka 40, 20-618 Lublin, Poland
⁴ Addiction Treatment Center of Lublin, Karłowicza 1, 20-027 Lublin, Poland
⁵ University of Economics and Innovations of Lublin, Institute of Psychology and Human Sciences, Projektowa 4, 20-209 Lublin, Poland

^* Corresponding author: m.borys@pollub.pl

Abstract

In the presented study the usefulness of eye-tracking data for classification of architectural spaces as stressful or relaxing was examined. The eye movements and pupillary response data were collected using the eye-tracker from 202 adult volunteers in the laboratory experiment in a well-controlled environment. Twenty features were extracted from the eye-tracking data and after the selection process the features were used in automated binary classification with a variety of machine learning classifiers including neural networks. The results of the classification using eye-tracking data features yielded 68% accuracy score, which can be considered satisfactory. Moreover, statistical analysis showed statistically significant differences in eye activity patterns between visualisations labelled as stressful or relaxing.